Top

Published in:

2019 | OriginalPaper | Chapter

A Practical Deep Online Ranking System in E-commerce Recommendation

Authors : Yan Yan, Zitao Liu, Meng Zhao, Wentao Guo, Weipeng P. Yan, Yongjun Bao

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

User online shopping experience in modern e-commerce websites critically relies on real-time personalized recommendations. However, building a productionized recommender system still remains challenging due to a massive collection of items, a huge number of online users, and requirements for recommendations to be responsive to user actions. In this work, we present our relevant, responsive, and scalable deep online ranking system (DORS) that we developed and deployed in our company. DORS is implemented in a three-level architecture which includes (1) candidate retrieval that retrieves a board set of candidates with various business rules enforced; (2) deep neural network ranking model that takes advantage of available user and item specific features and their interactions; (3) multi-arm bandits based online re-ranking that dynamically takes user real-time feedback and re-ranks the final recommended items in scale. Given a user as a query, DORS is able to precisely capture users’ real-time purchasing intents and help users reach to product purchases. Both offline and online experimental results show that DORS provides more personalized online ranking results and makes more revenue.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Implicit Linking of Food Entities in Social Media

next chapter Helping Your Docker Images to Spread Based on Explainable Models

Available only for authorised users

Real time is defined as under 200 ms.

Session is defined as a 30-min window in this paper.

The motivation for optimizing GMV is related to the e-commerce company’s business model. Since investors and shareholders utilize the settled GMV from all e-commerce transactions generated to estimate the current health and future potential of the corresponding company, maximizing GMV becomes critical. In general, if each item’s GMV sets to be \(\mathbf {1}\), optimizing GMV degrades to optimizing the sales conversion.

We could not release the exact number of operating servers due to the company confidentiality.

Agarwal, D., Chen, B.C., Elango, P.: Fast online learning through offline initialization for time-sensitive recommendation. In: KDD, pp. 703–712. ACM (2010)

Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002)CrossRef

Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Inf. Process.-Lett. Rev. 11(10), 203–224 (2007)

Burges, C., et al.: Learning to rank using gradient descent. In: ICML, pp. 89–96. ACM (2005)

Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: ICML, pp. 129–136. ACM (2007)

Chang, S., et al.: Streaming recommender systems. In: WWW, pp. 381–389 (2017)

Chen, C., Yin, H., Yao, J., Cui, B.: TeRec: a temporal recommender system over tweet stream. VLDB 6(12), 1254–1257 (2013)

Cherkassky, V., Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw. 17(1), 113–126 (2004)CrossRef

Davidson, J., et al.: The YouTube video recommendation system. In: RecSys, pp. 293–296. ACM (2010)

10.

Diaz-Aviles, E., Drumond, L., Schmidt-Thieme, L., Nejdl, W.: Real-time top-n recommendation in social streams. In: RecSys, pp. 59–66. ACM (2012)

11.

Ding, Y., Li, X.: Time weight collaborative filtering. In: CIKM, pp. 485–492. ACM (2005)

12.

Gao, H., Tang, J., Hu, X., Liu, H.: Exploring temporal effects for location recommendation on location-based social networks. In: RecSys, pp. 93–100. ACM (2013)

13.

Gomez-Uribe, C.A., Hunt, N.: The Netflix recommender system: algorithms, business value, and innovation. TMIS 6(4), 13 (2016)

14.

Guan, Z., Bu, J., Mei, Q., Chen, C., Wang, C.: Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In: SIGIR, pp. 540–547. ACM (2009)

15.

Gultekin, S., Paisley, J.: A collaborative Kalman filter for time-evolving dyadic processes. In: ICDM, pp. 140–149. IEEE (2014)

16.

Hannon, J., Bennett, M., Smyth, B.: Recommending Twitter users to follow using content and collaborative filtering approaches. In: RecSys, pp. 199–206. ACM (2010)

17.

Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)CrossRef

18.

Lang, K.: NewsWeeder: learning to filter netnews. In: Proceedings of the 12th International Machine Learning Conference (ML 1995) (1995)CrossRef

19.

Langford, J., Zhang, T.: The epoch-greedy algorithm for multi-armed bandits with side information. In: NIPS, pp. 817–824 (2008)

20.

Liu, Y., Miao, J., Zhang, M., Ma, S., Ru, L.: How do users describe their information need: query recommendation based on snippet click model. Expert Syst. Appl. 38(11), 13847–13856 (2011)

21.

Lu, D., et al.: Cross-media event extraction and recommendation. In: NAACL, pp. 72–76 (2016)

22.

Lu, Z., Agarwal, D., Dhillon, I.S.: A spatio-temporal approach to collaborative filtering. In: RecSys, pp. 13–20. ACM (2009)

23.

Rendle, S.: Factorization machines. In: ICDM, pp. 995–1000. IEEE (2010)

24.

Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: WWW, pp. 811–820. ACM (2010)

25.

Rendle, S., Schmidt-Thieme, L.: Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In: RecSys, pp. 251–258. ACM (2008)

26.

Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295. ACM (2001)

27.

Severyn, A., Moschitti, A.: Learning to rank short text pairs with convolutional deep neural networks. In: SIGIR, pp. 373–382. ACM (2015)

28.

Tang, J., Hu, X., Gao, H., Liu, H.: Exploiting local and global social context for recommendation. In: IJCAI, vol. 13, pp. 2712–2718 (2013)

29.

Tang, J., Hu, X., Liu, H.: Social recommendation: a review. Soc. Netw. Anal. Min. 3(4), 1113–1133 (2013)CrossRef

30.

Thompson, W.R.: On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25(3/4), 285–294 (1933)CrossRef

31.

Watkins, C.J.C.H.: Learning from delayed rewards. Ph.D. thesis, University of Cambridge, England (1989)

32.

Xiang, L., et al.: Temporal recommendation on graphs via long-and short-term preference fusion. In: KDD, pp. 723–732. ACM (2010)

33.

Xiong, L., Chen, X., Huang, T.K., Schneider, J., Carbonell, J.G.: Temporal collaborative filtering with bayesian probabilistic tensor factorization. In: SDM. pp. 211–222. SIAM (2010)

34.

Yin, D., Hong, L., Xue, Z., Davison, B.D.: Temporal dynamics of user interests in tagging systems. In: AAAI (2011)

35.

Yu, X., et al.: Personalized entity recommendation: A heterogeneous information network approach. In: WSDM. pp. 283–292. ACM (2014)

36.

Zhang, Y., Zhang, M., Liu, Y., Ma, S., Feng, S.: Localized matrix factorization for recommendation based on matrix block diagonal forms. In: WWW (2013)

Title: A Practical Deep Online Ranking System in E-commerce Recommendation
Authors: Yan Yan
Zitao Liu
Meng Zhao
Wentao Guo
Weipeng P. Yan
Yongjun Bao
Publisher: Springer International Publishing
Book: Machine Learning and Knowledge Discovery in Databases
Print ISBN: 978-3-030-10996-7

Electronic ISBN: 978-3-030-10997-4

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-10997-4_12

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner